• Science of Emotion and Sensibility
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• v.11 no.4
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• pp.637-644
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• 2008

Human emotion has been tried to be recognized by physiological measurements in developing emotion machine enabling to understand and react to user's emotion. This study is to find the time-dependent physiological measurements and their variation characteristics for discriminating emotions according to dimensional emotion model. Ten university students were asked to watch sixteen prepared images to evoke different emotions. Their subjective emotions and autonomic nervous responses such as ECG (electrocardiogram), PPG (photoplethysmogram), GSR (Galvanic skin response), RSP (respiration), and SKT(skin temperature) were measured during experiment. And these responses were analyzed into HR(Heart Rate), Respiration Rate, GSR amplitude average, SKT amplitude average, PPG amplitude, and PTT(Pulse Transition Time). TDPs(Time dependent parameters) defined as the delay, the activation, the half recovery and the full recovery of respective physiological signal in this study have been determined and statistically compared between variations from different emotions. The significant tendencies in TDP were shown between emotions. Therefore, TDP may provide useful measurements with emotion recognition.

• Journal of the Korea Fashion and Costume Design Association
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• v.14 no.1
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• pp.69-80
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• 2012

This research is to figure out how people recognize and perceive on cosmetic attributions of imported brand and domestic brand. In other words, we are trying to analyze the brand images and figure out how they are different in demographic perspective. For this research, this research has been fulfilled from September 20th 2011 to November 5th. We have surveyed women with age between 20's to 50's. With 477 data, we have analyzed by using SPSS 18.0 Version of statistic package. We have used frequency analysis, t-test and one way ANOVA (chi-square test) for data processing method. By comparing and analyzing the 16 attribute types of imported and domestic cosmetics, there were no attention difference on 7 questionaries' but there were attention difference on 9 questionnaires'. 5 questionaries' which show higher recognition on domestic brand of cosmetic than imported brand were shown. 4questionaries' which show higher recognition on imported brand of cosmetic than domestic brand were shown. By examining the different recognitions between domestic brand and imported brand of cosmetics in demographic perspective, it did not show any attentive difference on domestic brand by district residence, age, academic ability, marriage, occupation and monthly income average. But it showed attentive difference on imported cosmetic brand. It showed that people who live in capital region, with younger age and who have not been married have high rate of preference on imported cosmetic brand. It also showed that people who have higher academic ability and with higher monthly income average have high rate of preference on imported cosmetic brand. Especially, specialized job showed highest preference.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.219-223
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• 2020

To guarantee AI model's prominent recognition rate and recognition precision, obtaining the large number of data is essential. In this paper, we propose transfer learning-based object detection algorithm for maintaining outstanding performance even when the volume of training data is small. Also, we proposed a tranfer learning network combining Resnet-50 and YOLO(You Only Look Once) network. The transfer learning network uses the Leeds Sports Pose dataset to train the network that detects the person who occupies the largest part of each images. Simulation results yield to detection rate as 84% and detection precision as 97%.

• Journal of the Korean Society of Food Culture
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• v.17 no.4
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• pp.387-398
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• 2002

The purpose of the this study was to investigate the dietary behavior and the food habits of university freshman according to body mass index. Questionnaire were completed by 532 students in university freshman. The data were analyzed by SAS program. The results were as follows : The means of normal group's height, weight were $175.00{\pm}5.98cm,\;72.93{\pm}10.20kg$ for male students and $162.00{\pm}4.75cm,\;51.97{\pm}4.98kg$ for female students. Under 20 of BMI(body mass index) among students were 69.7% for male and 9.6% for female. $Twenty{\sim}twenty\;five(20{\sim}25)$ of BMI were 21.9% for male and 40.2% for female. The consumption of milk, oil and animal fat were significant according to BMI. There was high significant difference in the score of exercise and activity. In the self recognition of body shape 59.0% of male and 52.6% of female in normal weight group answered that their weight had to be a little thin. Self satisfaction rate was significantly higher in under weight group compared to normal weight group. This study suggest that a comprehensive nutrition education program is need for university students to improve desirable food habits and recognition of rate of figure.

• The Journal of the Korea Contents Association
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• v.7 no.2
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• pp.30-39
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• 2007

This paper is concerned with development of system to detect an obstructive vehicle which is an essential prerequisite for autonomous driving system of ASV(Advanced Safety Vehicle). First, the boundary of driving lanes is detected by a Kalman filter through the front image obtained by a CCD camera. Then, lanes are recognized by regression analysis of the detected boundary. Second, parameters of road curvature within the detected lane are used as input in error-BP algorithm to recognize the driving direction. Finally, an obstructive vehicle that enters into the detection region can be detected through setting detection fields of the front and lateral side. The experimental results showed that the proposed system has high accuracy more than 90% in the recognition rate of driving direction and the detection rate of an obstructive vehicle.

• The Journal of the Acoustical Society of Korea
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• v.23 no.3
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• pp.242-247
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• 2004

Conventional keyword spotting systems use the connected word recognition network consisted by keyword models and filler models in keyword spotting. This is why the system can not construct the language models of word appearance effectively for detecting keywords in large vocabulary continuous speech recognition system with large text data. In this paper to solve this problem, we propose a keyword spotting system using pseudo N-gram language model for detecting key-words and investigate the performance of the system upon the changes of the frequencies of appearances of both keywords and filler models. As the results, when the Unigram probability of keywords and filler models were set to 0.2, 0.8, the experimental results showed that CA (Correctly Accept for In-Vocabulary) and CR (Correctly Reject for Out-Of-Vocabulary) were 91.1% and 91.7% respectively, which means that our proposed system can get 14% of improved average CA-CR performance than conventional methods in ERR (Error Reduction Rate).

• Journal of the Korea Society of Computer and Information
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• v.15 no.10
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• pp.157-164
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• 2010

Recently, the Radio Frequency Identification(RFID) system has been growing with many promising features in technology and applications fields. Especially, a lot of efforts for the application of RFID system in the field of logistics management have been conducted. In addition, in logistics section of steel industry a remarkable efficiency can be attained by application of the RFID system. However, in the RFID system applied for the steel industry, lots of problems were found to be solved in recognition of the tags and antennas. This paper presents the feasibility of deploying RFID in the steel industry as a tool for reduction of the production cost. An application of the steel industry to RFID-based tracking management system was proposed. The results of this paper showed that the recognition rate of material input and output was found 100 percent and secured 99 percent of detection rate in the location. In conclusion, the proposed RFID-based tracking management system was approved superior to the existing system in terms of productivity.

• Journal of the Korea Society of Computer and Information
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• v.15 no.10
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• pp.27-34
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• 2010

This paper proposes an efficient method for verifying palmprint which is captured at the natural interface without any physical restriction. The location and orientation of the region of interest (ROI) in palm images are variously appeared due to the translation and rotation of hand. Therefore, it is necessary to extract the ROI stably for palmprint recognition. This paper presents a method that can extract the ROI, which is based on the reference points that are located at the center of the crotch segments between index finger and middle finger and between ring finger and little finger. It also proposes a palmprint recognition method using the histogram of local binary patterns (LBP). Experiments for evaluating the performance of the proposed method were performed on 1,597 palmprint images acquired from 100 different persons. The experimental results showed that ROI was correctly extracted at the rate of 99.5% and the equal error rate (EER) and the decidability index d' indicating the performance of palmprint verification were 0.136 and 3.539, respectively. These results demonstrate that the proposed method is robust to the variations of the translation and rotation of hand.

• Journal of the Korea Society of Computer and Information
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• v.12 no.6
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• pp.167-175
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• 2007

Fuzzy logic is used to represent qualitative knowledge and provides interpretability to a controlling system model in bioinformatics. This paper focuses on a bioinformatics data classification which is an important bioinformatics application. This paper reviews the two traditional controlling system models The sequence-based threshold controller have problems of optimal range decision for threshold readjustment and long processing time for optimal threshold induction. And the binary-based threshold controller does not guarantee for early system stability in the GPCR data classification for optimal threshold induction. To solve these problems, we proposes a fuzzy-based threshold controller for ART1 clustering in GPCR classification. We implement the proposed method and measure processing time by changing an induction recognition success rate and a classification threshold value. And, we compares the proposed method with the sequence-based threshold controller and the binary-based threshold controller The fuzzy-based threshold controller continuously readjusts threshold values with membership function of the previous recognition success rate. The fuzzy-based threshold controller keeps system stability and improves classification system efficiency in GPCR classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.1
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• pp.144-150
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• 2017

Recently, deep learning is used for intelligent processing and accuracy improvement of data. It is formed calculation model composed of multi data processing layer that train the data representation through an abstraction of the various levels. A category of deep learning, convolution neural network is utilized in various research fields, which are human pose estimation, face recognition, image classification, speech recognition. When using the deep layer and lots of class, CNN that show a good performance on image classification obtain higher classification rate but occur the overfitting problem, when using a few data. So, we design the training network based on convolution neural network and trained our image data set for object classification in few class problem. The experiment show the higher classification rate of 7.06% in average than the previous networks designed to classify the object in 1000 class problem.